1. Field of the Invention
The present invention relates to communication field, and more specifically, to method and apparatus for spectrum management in xDSL system.
2. Description of the Related Art
xDSL, which refers to various Digital Subscriber Line, is a high-speed data transmission technique over telephone twisted pair (Unshielded Twisted Pair, UTP). For years of development, xDSL has evolved from a first generation of Asymmetrical Digital Subscriber Line (ADSL) to the current second generation ADSL2, ADSL2+, as well as the more advanced Very-high-bit-rate Digital Subscriber Line (VDSL) and VDSL2. ADSL and VDSL are a type of xDSL system which adopts a Discrete Multi-Tone Modulation (DMT) to divide the frequency into several non-overlapping subchannels. Each subchannel is designated for upstream transmission or downstream transmission. Each subchannel corresponds to a carrier with different frequency and conducts a QAM modulation on each different carrier. Such frequency division significantly facilitates DSL designs.
In various xDSL techniques, except for baseband DSL, such as, Internet Digital Subscriber Line (IDSL) and Symmetrical Highbit Digital Subscriber Line (SHDSL), passband xDSL adopts frequency division multiplex (FDM) technique to allow xDSL and Plain Old Telephone Service (POTS) to coexist on a same pair of twisted wires, in which the xDSL takes up the high frequency bands while the POTS takes up frequency baseband below 4 KHz. The POTS signal and the xDSL signal are separated by a splitter. Passband xDSL adopts DMT to perform modulation and demodulation.
The system providing multiple xDSL accesses is referred to as a Digital Subscriber Line Access Multiplexer (DSLAM). A reference module for DSLAM is illustrated in FIG. 1. FIG. 1 illustrates a prior art reference module of xDSL system 100.
As illustrated in FIG. 1, a DSLAM 120 includes a user end transceiving unit 122 and a splitter/integrator 124. In upstream direction, the user end transceiving unit 122 receives a DSL signal from a computer 110, amplifies the received the signal, and transmits the processed DSL signal to the splitter/integrator 124. The splitter/integrator 124 integrates the DSL signal from the user end transceiving unit 122 and a POTS signal from a telephone terminal 130. The integrated signals are transmitted via a multiple UTP 140 and are received by a splitter/integrator 152 in a remote DSLAM 150. The splitter/integrator 152 separates the received signals. The POTS signal in the received signals is transmitted to the Public Switched Telephone Network (PSTN) 160. The DSL signal in the received signals is transmitted to a transceiving unit 154 of the DSLAM 150. Then, the transceiving unit 154 amplifies the received signal and transmits the amplified signal to a Network Management System (NMS) 170. In downstream direction, signals are transferred in a reverse order.
With the improvement of the xDSL technology on the spectrum usage, crosstalk, especially on a high frequency band, is becoming a prominent issue.
FIG. 2 is a diagram illustrating Far End Crosstalk and Near End Crosstalk in accordance with related arts.
A subscriber cable typically includes multiple pairs of twisted wires (25 pairs or more). A plurality of different services may run on each twisted pair wire. Crosstalk may occur when different types of xDSLs run at the same time. The performance of some lines may degrade dramatically due to this reason. For longer wires, some wires may not be able to provide any form of DSL service. Crosstalk is a dominant factor affecting the user rate in the current DSL modem (e.g., ADSL, VDSL) system. Crosstalk can be divided into Far End Crosstalk (FEXT) and Near End Crosstalk (NEXT), as illustrated in FIG. 2. Usually, the influence exerted by NEXT is greater than by FEXT. However, in ADSL/VDSL, due to the use of technique which divides frequency by upstream and downstream and due to the use of frequency division multiplex technique, the influence of FEXT is far greater than NEXT, especially in a Central Office (CO)/Remote Terminal (RT) scenario where CO and RT are used in mixture.
The crosstalk is analyzed below in detail.
In a communication model with a DMT mode, N users, K tones, the signal at each tone of the receiving end can be independently modeled as:yk=Hkxk+σk  (1)    where Hk: denotes an N*N transfer matrix on tone k.
yk: denotes the signal received by a certain user on tone k.
xk: denotes the signal transmitted by a certain user on tone k.
σk: denotes a noise signal of a certain user on tone k.
In normal cases, a receiving end of each xDSL modem treats the interferences imposed by other modems as noise. The data rate of user n achieved on tone k can be calculated according to Shannon formula for channel capacity.
                              b          k          n                =                              log            2                    (                      1            +                                                                                                                          h                      k                                              n                        ,                        n                                                                                                  2                                ⁢                                  s                  k                  n                                                                                                  ∑                                          m                      ≠                      n                                                        ⁢                                                                                                                                      h                          k                                                      n                            ,                            m                                                                                                                      2                                        ⁢                                          s                      k                      m                                                                      +                                  σ                  k                  n                                                              )                                    (        2        )                where skn: denotes the transmit power of subscriber line n on tune k.
hkn,m: denotes a channel crosstalk function from user m to user n on tune k.
hkn,n: denotes a transmit function of user n on tone k.
σkn: denotes the noise signal of user n on tune k; and
bkn: denotes the achievable number of bits loaded on subscriber line n on tone k.
The outcome of formula (2) is the number of bits loaded on each tone, i.e., the user data rate achievable on tone k. For a severe crosstalk, the skm may be larger, which leads to a small outcome derived from the whole formula, decreasing thereby the line rate. As can be seen from formula (2), crosstalk has a severe impact on the transmit capacity. In other words, the line rate is decreased.
As shown in FIG. 2, since the upstream and downstream channels of xDSL adopt FDM mode, the NEXT may not exert too much impact on the system performance. However, FEXT may impair severely the transmission performance of the lines. When multi-users in a bundle of cables require to activate xDSL service, FEXT may lead to a low line rate and unstable performance, or even the inability to serve the services, etc, which ultimately leads to a low DSLAM line activation rate.
DSM technology cancels or attenuates noise by adjusting the transmit power of each frequency band so as to accelerate the data rate. A traditional power adjusting method is a static spectrum management method including a Plat Power Back-Off, a Reference PSD Method and a Reference Noise Method, etc. Dynamic Spectrum Management (DSM) is a recently proposed method for managing power allocation more effectively. Such method overcomes the drawbacks of the static spectrum management method and is able to cancel or attenuate the influence of crosstalk among each user by dynamically adjusting the power so that the line rate can be accelerated significantly. Especially, in the case where CO and RT are used in mixture, the crosstalk from the short lines has a more severe impact on the long lines.
The purpose of the DSM is to adjust the spectrum control parameter in real time or periodically without violating the spectrum compatibility so that the system can operate in an optimal state.
Specifically, the purpose of DSM is to cancel the crosstalk by automatically adjusting the transmit power on each modem in the same cable. Particularly, in the case where CO and RT are used in mixture, the short lines have a more severe crosstalk impact on the long lines. As illustrated in FIG. 3, the impact from line 2 on line 1 is greater than the impact from line 1 on line 2. In FIG. 3, the lines are numbered as 1, 2, 3, 4 from top to down. An object of the DSM is to allow each modem to achieve a balance between the maximum achievable rate and the minimum of crosstalk impact on the other modems by adjusting the transmit power of each modem.
DSM methods can be categorized into two types. The first type is a Central Office based method, for instance, Optimum Spectrum Balancing (OSB) algorithm, Iterative Spectrum Balancing (ISB) algorithm, etc. The second type is a distributed method, such as, Iterative Water Filling (IWF), Autonomous Spectrum Balancing (ASB), etc. These two types of algorithms are illustrated in detail.
OSB method is a DSM optimization method. Suppose that there are N users, and each user has K tones, where N−1 users have a target rate. The basic problem of DSM can be expressed as to enhance the rate of the first user as fast as possible while guarantee the rates of N−1 users. Meanwhile, each signal power should meet the requirement of maximum power spectrum density (PSD), and the total power of each user should meet a corresponding constraint
                    ∑                  k          =          1                K            ⁢              S        k        n              ≤          P      n        ,where Pn is a maximum allowable transmit power for user n. Due to the nonconvexity of this problem, all possible value for skn need to be enumerated for obtaining all the solutions to the problem directly. Consequently, the algorithm results in an exponential computation problem in both user N and tone K, i.e. O(eKN). OSB adopts a coupling method to formulate the above problem as
                              J          =                                                    max                                                      s                    1                                    ,                                                                          ⁢                  …                  ,                                                                          ⁢                                      s                    N                                                              ⁢                              R                1                                      +                                          ∑                                  n                  =                  2                                N                            ⁢                                                w                  n                                ⁢                                  R                  n                                                      -                                          ∑                                  n                  =                  1                                N                            ⁢                                                ∑                                      k                    =                    1                                    K                                ⁢                                                      λ                    n                                    ⁢                                      s                    k                    n                                                                                      ⁢                                  ⁢                                                            s                .                t                            ⁢              .0                        ≤                          s              k              n                        ≤                          s              max                                ,                      k            =            1                    ,          …          ⁢                                          ,                      K            ;                          n              =              1                                ,          …          ⁢                                          ,          N                                    (        3        )            where skn: denotes the transmit power of subscriber line n on tone k.
      R    n    =            ∑              k        =        1            K        ⁢                  b        k        n            :      denotes a total rate of subscriber line n.
wn, λn are Lagrangian operators respectively.
The target function in formula (3) can further be transformed to
                                                        J              =                            ⁢                                                                    ∑                                          k                      =                      1                                        K                                    ⁢                                      b                    k                    1                                                  +                                                      ∑                                          n                      =                      2                                        N                                    ⁢                                                            ∑                                              k                        =                        1                                            K                                        ⁢                                                                  w                        n                                            ⁢                                              b                        k                        n                                                                                            -                                                      ∑                                          n                      =                      1                                        N                                    ⁢                                                            ∑                                              k                        =                        1                                            K                                        ⁢                                                                  λ                        n                                            ⁢                                              s                        k                        n                                                                                                                                                                    =                            ⁢                                                ∑                                      k                    =                    1                                    K                                ⁢                                  (                                                            ∑                                              n                        =                        1                                            N                                        ⁢                                          (                                                                                                    w                            n                                                    ⁢                                                      b                            k                            n                                                                          -                                                                              λ                            n                                                    ⁢                                                      s                            k                            n                                                                                              )                                                        )                                                                                                        =                            ⁢                                                ∑                                      k                    =                    1                                    K                                ⁢                                  J                  k                                                                                        (        6        )                                          J          k                =                              ∑                          n              =              1                        N                    ⁢                      (                                                            w                  n                                ⁢                                  b                  k                  n                                            -                                                λ                  n                                ⁢                                  s                  k                  n                                                      )                                              (        7        )            where
skn denotes the transmit power of subscriber line n on tune k.
bkn: denotes the achievable number of bits loaded on subscriber line n on tone k.
wn, λn are Lagrangian operators respectively.
Here w1=1, considering Jk is only related with the power allocation sk1, sk2, . . . , skN on tune k, and is irrelevant with the power allocation on other tunes, we only need to enumerate the power allocation for each user on tone k to calculate the maximum Jk. An optimal solution to J can be obtained by solving max Jk for each independent tone respectively. OSB reduces the original computational complexity to O(KeN) while guarantees to find the optimal solution.
The detailed implementation of DSM will be illustrated in connection with FIG. 4.
FIG. 4 illustrates a DSM reference model according to related arts.
The mainstream architecture of the first layer of DSM includes a Spectrum Management Center (SMC) and three control interfaces, namely, DSM-S, DSM-C, DSM-D. SMC reads DSL parameters such as operating state from DSL-LT via interface DSM-D. The SMC exchanges information with associated SMC via the interface DSM-S. With sufficient information, the SMC performs a series of optimization algorithms and then delivers control parameters via DSM-C to the DSL-LT. As such the line may operate at its optimal state.
The detailed implementation of OSB is illustrated in connection with FIGS. 5 and 6.
FIG. 5 illustrates a flowchart for implementing the OSB method according to related arts. FIG. 6 illustrates a process for solving a mathematical model for OSB method according to related arts.
As illustrated in FIG. 5, the process for implementing the OSB method includes the following steps.
Step S502: A mathematical model
      J    k    =            ∑              n        =        1            N        ⁢          (                                    w            n                    ⁢                      b            k            n                          -                              λ            n                    ⁢                      s            k            n                              )      is constructed.
Step S504: sk1, sk2, . . . skN that render Jk at its maximum is calculated. The solving process is shown in FIG. 6. Enumeration is performed on all skn on space [0, smax]N with a certain granularity until convergence. That is, calculations are performed upon each tone k to obtain sk1, sk2, . . . , skN which render Jk at its maximum until all the constraints are met and there are no more changes to the power allocation. For each user n, wn and λn are adjusted dynamically according to how the constraints are met.
Step S506: Transmit power of each modem in the same cable is adjusted according to the derived sk1, sk2, . . . , skN.
In the solving process as shown in FIG. 6, when calculating sk1, sk2, . . . , skN for the maximum Jk, since Jk is a nonconvex function, there is no simple analytic solution. Therefore, in order to obtain the optimal sk1, sk2, . . . , skN, enumeration needs to be performed on all skn on the [0, smax]N space. When a round of enumeration is over, wn and λn will be adjusted depends on how the constraints are met. If the constraints are met already, the value of wn or λn for a corresponding subscriber line may be reduced so as to minimize the impact exerted by this portion on the whole target function. If the constraints are not met yet, the value of wn or λn for a corresponding subscriber line needs to be increased so as to increase the weight of this portion in the whole target function. The algorithm is repeated until all the constraints are met and there is no more change to the power allocation. At this point the algorithm can be regarded as convergent. The proof of the convergence of the algorithm is described in R. Cendrillon, W. Yu, M. Moonen, J. Verlinden, and T. Bostoen, “Optimal multi-user spectrum management for digital subscriber lines” accepted by IEEE Transactions on Communications, 2005.
The OSB is an optimization algorithm. When the number of users N is not too large, the calculation result can be obtained in an acceptable time period. However, when the number of users increase, the computational time is increased exponentially, which may not be tolerated in a while. In other words, the advantages of the OSB lie in optimality and calculability in case N is small. The disadvantages of OBS lie in exponential complexity, non-autonomy, and requirement for data interaction with a center manager.
An alternative DSM method is IWF method.
IWF method refers to Iterative Water Filling method which is a type of greedy algorithm. It only takes into consideration the impact of the change of skn on the line rate of user n, rather than the interference on other lines from optimization perspective. Its target function Jk can be expressed as Jk≅Jkn=wnbkn−λnskn.
FIG. 7 illustrates a flowchart for implementing the IWF method according to related arts. FIG. 8 illustrates a process for solving a mathematical model for IWF method according to related arts.
As illustrated in FIG. 7, the IWF method may be detailed as follows.
Step S202: A mathematical model Jk≅Jkn=wnbkn−λnskn is constructed.
Step S204: sk1, sk2, . . . skN that render Jk at its maximum is calculated. The solving process is illustrated in FIG. 8, which is omitted herein for brevity.
Step S206: The transmit power of each modem on the same cable is adjusted according to the calculated sk1, sk2, . . . , skN.
As mentioned above, the solution searched by IWF algorithm is a local optimal solution, rather than a global optimal solution. The calculation result is not as good as that of OSB.
The computational complexity regarding the IWF method is relatively low. For a larger N and K, the computation can also be carried out. Moreover, it is completely autonomous. That is, each user only needs to optimize its own rate and satisfies it own power constraint, instead of exchanging data information with different users. In other words, it does not need a center manager, which is feasible in practical system.
In short, the advantages of the IWF lie in low computational complexity, autonomy, no need for a center manager, feasibility. The disadvantages of IWF lie in that it is greedy and can not ensure an optimal solution or an approximate optimal solution.
Table 3 lists a comparison between OSB algorithm and IWF algorithm regarding advantages and disadvantage.
TABLE 3comparison between OSB algorithm and IWF algorithmAlgorithmAdvantagesDisadvantagesOSBoptimality, andexponential complexity in N,(optimizationcalculability innon-autonomy, and requirementalgorithm)case N is smallfor data interaction with acenter managerIWFlow computationalgreedy and can not ensure an(greedycomplexity, autonomy,optimal solution or can onlyalgorithm)no need for a centerapproximate optimal solutionmanager, feasibility
The aforementioned OSB method and the IWF method are representative. More simplified methods related to OSB and extended methods related to IWF, etc, can be included.
As mentioned above, the first type of algorithm stands on a global optimization perspective. According to this algorithm a quite optimal result regardless of the environment may always be found. However, the algorithm is quite complex and the computational complexity increases exponentially as the number of users increases. Moreover, equipment such as a center controller is needed, which increases the cost. The second type does not stand on a global optimization perspective. With this algorithm, local optimal result may be obtained. The algorithm is quite simple and does not require additional equipment.
FIG. 9 illustrates a scenario having a rather severe crosstalk. In this scenario, one subscriber line has a greater crosstalk impact on the other subscriber line. As illustrated in FIG. 9, for a signal transmitted from ONU to be coupled to user 1 lines, the signal needs to go though a relatively short distance of attenuation, which causes a considerable crosstalk. For a signal transmitted from the Central Office to be coupled to user 2 line, the signal needs to go through a relatively long distance of attenuation. Accordingly, in this case, user2 has a greater crosstalk impact on user 1. Such loop scenario where the impact of crosstalk imposed by one subscriber line on the other subscriber line differs dramatically is considered as a severe scenario.
FIG. 10 illustrates a comparison between the OSM and IWF in a severe crosstalk scenario.
When the crosstalk is not that severe, the result of the second type of algorithm (or the distributed method) is substantially close to that of the first type of algorithm (or the center controller method). However, when the crosstalk is severe, the second type of algorithm may obtain a far poorer result than from the first type of algorithm. In the case of a poor scenario as illustrated in FIG. 10, the IWF algorithm of the second type of algorithm has a far poorer performance than the OSM algorithm of the first type of algorithm.
Therefore, it is desirable to make improvement to the IWF method so that IWF may approximate the OSM performance while still remain its simple complexity, thereby achieving ideal autonomous spectrum management.